Overview

Dataset statistics

Number of variables14
Number of observations731282
Missing cells1
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory78.1 MiB
Average record size in memory112.0 B

Variable types

Numeric11
Categorical3

Alerts

name has a high cardinality: 731281 distinct valuesHigh cardinality
joined has a high cardinality: 2125 distinct valuesHigh cardinality
days_watched is highly overall correlated with mean_score and 8 other fieldsHigh correlation
mean_score is highly overall correlated with days_watched and 5 other fieldsHigh correlation
watching is highly overall correlated with days_watched and 7 other fieldsHigh correlation
completed is highly overall correlated with days_watched and 8 other fieldsHigh correlation
on_hold is highly overall correlated with days_watched and 6 other fieldsHigh correlation
dropped is highly overall correlated with days_watched and 6 other fieldsHigh correlation
plan_to_watch is highly overall correlated with days_watched and 7 other fieldsHigh correlation
total_entries is highly overall correlated with days_watched and 8 other fieldsHigh correlation
rewatched is highly overall correlated with days_watched and 3 other fieldsHigh correlation
episodes_watched is highly overall correlated with days_watched and 8 other fieldsHigh correlation
days_watched is highly skewed (γ1 = 586.8567722)Skewed
watching is highly skewed (γ1 = 44.34280853)Skewed
on_hold is highly skewed (γ1 = 77.81276188)Skewed
dropped is highly skewed (γ1 = 158.5621788)Skewed
plan_to_watch is highly skewed (γ1 = 45.40712855)Skewed
rewatched is highly skewed (γ1 = 138.5021426)Skewed
episodes_watched is highly skewed (γ1 = 521.43819)Skewed
name is uniformly distributedUniform
id has unique valuesUnique
days_watched has 356112 (48.7%) zerosZeros
mean_score has 376196 (51.4%) zerosZeros
watching has 406513 (55.6%) zerosZeros
completed has 369391 (50.5%) zerosZeros
on_hold has 531300 (72.7%) zerosZeros
dropped has 539661 (73.8%) zerosZeros
plan_to_watch has 472247 (64.6%) zerosZeros
total_entries has 340242 (46.5%) zerosZeros
rewatched has 590483 (80.7%) zerosZeros
episodes_watched has 352107 (48.1%) zerosZeros

Reproduction

Analysis started2024-01-02 00:05:17.236989
Analysis finished2024-01-02 00:06:06.883535
Duration49.65 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

id
Real number (ℝ)

Distinct731282
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean507023.55
Minimum1
Maximum1291097
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2024-01-01T21:06:06.979534image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile38127.05
Q1201112.5
median425173.5
Q3775344.5
95-th percentile1198874.9
Maximum1291097
Range1291096
Interquartile range (IQR)574232

Descriptive statistics

Standard deviation364015.03
Coefficient of variation (CV)0.717945
Kurtosis-0.82203269
Mean507023.55
Median Absolute Deviation (MAD)264887.5
Skewness0.56779944
Sum3.707772 × 1011
Variance1.3250694 × 1011
MonotonicityNot monotonic
2024-01-01T21:06:07.147499image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
599463 1
 
< 0.1%
599423 1
 
< 0.1%
599425 1
 
< 0.1%
599427 1
 
< 0.1%
599429 1
 
< 0.1%
599431 1
 
< 0.1%
599433 1
 
< 0.1%
599435 1
 
< 0.1%
599437 1
 
< 0.1%
Other values (731272) 731272
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
9 1
< 0.1%
18 1
< 0.1%
20 1
< 0.1%
23 1
< 0.1%
36 1
< 0.1%
44 1
< 0.1%
47 1
< 0.1%
ValueCountFrequency (%)
1291097 1
< 0.1%
1291091 1
< 0.1%
1291087 1
< 0.1%
1291085 1
< 0.1%
1291083 1
< 0.1%
1291079 1
< 0.1%
1291071 1
< 0.1%
1291065 1
< 0.1%
1291063 1
< 0.1%
1291057 1
< 0.1%

name
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct731281
Distinct (%)100.0%
Missing1
Missing (%)< 0.1%
Memory size5.6 MiB
xinil
 
1
dim-senpai
 
1
dc_lov
 
1
vernfarrell24
 
1
tori15
 
1
Other values (731276)
731276 

Length

Max length21
Median length15
Mean length9.906259
Min length3

Characters and Unicode

Total characters7244259
Distinct characters38
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique731281 ?
Unique (%)100.0%

Sample

1st rowxinil
2nd rowaokaado
3rd rowcrystal
4th rowarcane
5th rowmad

Common Values

ValueCountFrequency (%)
xinil 1
 
< 0.1%
dim-senpai 1
 
< 0.1%
dc_lov 1
 
< 0.1%
vernfarrell24 1
 
< 0.1%
tori15 1
 
< 0.1%
jermainevale922 1
 
< 0.1%
syyslyyra 1
 
< 0.1%
burst 1
 
< 0.1%
donnymccullo615 1
 
< 0.1%
kacper6464 1
 
< 0.1%
Other values (731271) 731271
> 99.9%

Length

2024-01-01T21:06:07.339499image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
34
 
< 0.1%
zero 8
 
< 0.1%
akira 8
 
< 0.1%
yuuki 8
 
< 0.1%
dante 7
 
< 0.1%
gabriel 7
 
< 0.1%
yuki 7
 
< 0.1%
aya 7
 
< 0.1%
lawliet 6
 
< 0.1%
lucy 6
 
< 0.1%
Other values (727382) 731183
> 99.9%

Most occurring characters

ValueCountFrequency (%)
a 694263
 
9.6%
e 600784
 
8.3%
i 490857
 
6.8%
r 469196
 
6.5%
n 450486
 
6.2%
o 434226
 
6.0%
s 355304
 
4.9%
l 339725
 
4.7%
t 288602
 
4.0%
m 235243
 
3.2%
Other values (28) 2885573
39.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6389818
88.2%
Decimal Number 770800
 
10.6%
Connector Punctuation 52805
 
0.7%
Dash Punctuation 30836
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 694263
 
10.9%
e 600784
 
9.4%
i 490857
 
7.7%
r 469196
 
7.3%
n 450486
 
7.1%
o 434226
 
6.8%
s 355304
 
5.6%
l 339725
 
5.3%
t 288602
 
4.5%
m 235243
 
3.7%
Other values (16) 2031132
31.8%
Decimal Number
ValueCountFrequency (%)
1 174686
22.7%
2 125249
16.2%
0 76351
9.9%
3 68966
 
8.9%
9 67031
 
8.7%
4 56610
 
7.3%
7 53594
 
7.0%
8 50610
 
6.6%
6 49955
 
6.5%
5 47748
 
6.2%
Connector Punctuation
ValueCountFrequency (%)
_ 52805
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 30836
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6389818
88.2%
Common 854441
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 694263
 
10.9%
e 600784
 
9.4%
i 490857
 
7.7%
r 469196
 
7.3%
n 450486
 
7.1%
o 434226
 
6.8%
s 355304
 
5.6%
l 339725
 
5.3%
t 288602
 
4.5%
m 235243
 
3.7%
Other values (16) 2031132
31.8%
Common
ValueCountFrequency (%)
1 174686
20.4%
2 125249
14.7%
0 76351
8.9%
3 68966
 
8.1%
9 67031
 
7.8%
4 56610
 
6.6%
7 53594
 
6.3%
_ 52805
 
6.2%
8 50610
 
5.9%
6 49955
 
5.8%
Other values (2) 78584
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7244259
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 694263
 
9.6%
e 600784
 
8.3%
i 490857
 
6.8%
r 469196
 
6.5%
n 450486
 
6.2%
o 434226
 
6.0%
s 355304
 
4.9%
l 339725
 
4.7%
t 288602
 
4.0%
m 235243
 
3.2%
Other values (28) 2885573
39.8%

gender
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.6 MiB
-
506907 
male
126982 
female
96479 
non-binary
 
914

Length

Max length10
Median length1
Mean length2.1918343
Min length1

Characters and Unicode

Total characters1602849
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmale
2nd rowmale
3rd rowfemale
4th row-
5th row-

Common Values

ValueCountFrequency (%)
- 506907
69.3%
male 126982
 
17.4%
female 96479
 
13.2%
non-binary 914
 
0.1%

Length

2024-01-01T21:06:07.473499image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-01T21:06:07.608502image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
506907
69.3%
male 126982
 
17.4%
female 96479
 
13.2%
non-binary 914
 
0.1%

Most occurring characters

ValueCountFrequency (%)
- 507821
31.7%
e 319940
20.0%
a 224375
14.0%
m 223461
13.9%
l 223461
13.9%
f 96479
 
6.0%
n 2742
 
0.2%
o 914
 
0.1%
b 914
 
0.1%
i 914
 
0.1%
Other values (2) 1828
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1095028
68.3%
Dash Punctuation 507821
31.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 319940
29.2%
a 224375
20.5%
m 223461
20.4%
l 223461
20.4%
f 96479
 
8.8%
n 2742
 
0.3%
o 914
 
0.1%
b 914
 
0.1%
i 914
 
0.1%
r 914
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
- 507821
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1095028
68.3%
Common 507821
31.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 319940
29.2%
a 224375
20.5%
m 223461
20.4%
l 223461
20.4%
f 96479
 
8.8%
n 2742
 
0.3%
o 914
 
0.1%
b 914
 
0.1%
i 914
 
0.1%
r 914
 
0.1%
Common
ValueCountFrequency (%)
- 507821
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1602849
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 507821
31.7%
e 319940
20.0%
a 224375
14.0%
m 223461
13.9%
l 223461
13.9%
f 96479
 
6.0%
n 2742
 
0.2%
o 914
 
0.1%
b 914
 
0.1%
i 914
 
0.1%
Other values (2) 1828
 
0.1%

joined
Categorical

Distinct2125
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.6 MiB
2011-11-09t00:00:00+00:00
 
3892
2011-11-21t00:00:00+00:00
 
3828
2011-11-22t00:00:00+00:00
 
3798
2011-11-08t00:00:00+00:00
 
3674
2011-11-17t00:00:00+00:00
 
3563
Other values (2120)
712527 

Length

Max length25
Median length25
Mean length25
Min length25

Characters and Unicode

Total characters18282050
Distinct characters14
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique123 ?
Unique (%)< 0.1%

Sample

1st row2004-11-05t00:00:00+00:00
2nd row2004-11-11t00:00:00+00:00
3rd row2004-11-13t00:00:00+00:00
4th row2004-12-05t00:00:00+00:00
5th row2005-01-03t00:00:00+00:00

Common Values

ValueCountFrequency (%)
2011-11-09t00:00:00+00:00 3892
 
0.5%
2011-11-21t00:00:00+00:00 3828
 
0.5%
2011-11-22t00:00:00+00:00 3798
 
0.5%
2011-11-08t00:00:00+00:00 3674
 
0.5%
2011-11-17t00:00:00+00:00 3563
 
0.5%
2011-11-10t00:00:00+00:00 3536
 
0.5%
2011-11-11t00:00:00+00:00 3519
 
0.5%
2011-11-07t00:00:00+00:00 3499
 
0.5%
2011-11-14t00:00:00+00:00 3458
 
0.5%
2011-11-18t00:00:00+00:00 3421
 
0.5%
Other values (2115) 695094
95.1%

Length

2024-01-01T21:06:07.731533image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2011-11-09t00:00:00+00:00 3892
 
0.5%
2011-11-21t00:00:00+00:00 3828
 
0.5%
2011-11-22t00:00:00+00:00 3798
 
0.5%
2011-11-08t00:00:00+00:00 3674
 
0.5%
2011-11-17t00:00:00+00:00 3563
 
0.5%
2011-11-10t00:00:00+00:00 3536
 
0.5%
2011-11-11t00:00:00+00:00 3519
 
0.5%
2011-11-07t00:00:00+00:00 3499
 
0.5%
2011-11-14t00:00:00+00:00 3458
 
0.5%
2011-11-18t00:00:00+00:00 3421
 
0.5%
Other values (2115) 695094
95.1%

Most occurring characters

ValueCountFrequency (%)
0 9269567
50.7%
: 2193846
 
12.0%
1 1536344
 
8.4%
- 1462564
 
8.0%
2 1218583
 
6.7%
t 731282
 
4.0%
+ 731282
 
4.0%
9 254437
 
1.4%
8 213998
 
1.2%
3 157384
 
0.9%
Other values (4) 512763
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13163076
72.0%
Other Punctuation 2193846
 
12.0%
Dash Punctuation 1462564
 
8.0%
Lowercase Letter 731282
 
4.0%
Math Symbol 731282
 
4.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9269567
70.4%
1 1536344
 
11.7%
2 1218583
 
9.3%
9 254437
 
1.9%
8 213998
 
1.6%
3 157384
 
1.2%
7 143286
 
1.1%
4 138441
 
1.1%
5 117751
 
0.9%
6 113285
 
0.9%
Other Punctuation
ValueCountFrequency (%)
: 2193846
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1462564
100.0%
Lowercase Letter
ValueCountFrequency (%)
t 731282
100.0%
Math Symbol
ValueCountFrequency (%)
+ 731282
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 17550768
96.0%
Latin 731282
 
4.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9269567
52.8%
: 2193846
 
12.5%
1 1536344
 
8.8%
- 1462564
 
8.3%
2 1218583
 
6.9%
+ 731282
 
4.2%
9 254437
 
1.4%
8 213998
 
1.2%
3 157384
 
0.9%
7 143286
 
0.8%
Other values (3) 369477
 
2.1%
Latin
ValueCountFrequency (%)
t 731282
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18282050
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9269567
50.7%
: 2193846
 
12.0%
1 1536344
 
8.4%
- 1462564
 
8.0%
2 1218583
 
6.7%
t 731282
 
4.0%
+ 731282
 
4.0%
9 254437
 
1.4%
8 213998
 
1.2%
3 157384
 
0.9%
Other values (4) 512763
 
2.8%

days_watched
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct5033
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.180819
Minimum0
Maximum105338.6
Zeros356112
Zeros (%)48.7%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2024-01-01T21:06:07.872499image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.2
Q324.8
95-th percentile120
Maximum105338.6
Range105338.6
Interquartile range (IQR)24.8

Descriptive statistics

Standard deviation140.10507
Coefficient of variation (CV)5.7940582
Kurtosis437015.32
Mean24.180819
Median Absolute Deviation (MAD)0.2
Skewness586.85677
Sum17682998
Variance19629.432
MonotonicityNot monotonic
2024-01-01T21:06:08.042500image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 356112
48.7%
0.4 5675
 
0.8%
0.2 5334
 
0.7%
0.1 4228
 
0.6%
0.6 2770
 
0.4%
0.9 2722
 
0.4%
0.8 2636
 
0.4%
0.5 2406
 
0.3%
1.3 2043
 
0.3%
0.3 2007
 
0.3%
Other values (5023) 345349
47.2%
ValueCountFrequency (%)
0 356112
48.7%
0.1 4228
 
0.6%
0.2 5334
 
0.7%
0.3 2007
 
0.3%
0.4 5675
 
0.8%
0.5 2406
 
0.3%
0.6 2770
 
0.4%
0.7 1873
 
0.3%
0.8 2636
 
0.4%
0.9 2722
 
0.4%
ValueCountFrequency (%)
105338.6 1
< 0.1%
15194 1
< 0.1%
10384.9 1
< 0.1%
10005.1 1
< 0.1%
10004.3 1
< 0.1%
10000 2
< 0.1%
9010.7 1
< 0.1%
8015.3 1
< 0.1%
7835.9 1
< 0.1%
7378.6 1
< 0.1%

mean_score
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct731
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9476831
Minimum0
Maximum10
Zeros376196
Zeros (%)51.4%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2024-01-01T21:06:08.204499image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q38.04
95-th percentile9.5
Maximum10
Range10
Interquartile range (IQR)8.04

Descriptive statistics

Standard deviation4.1271836
Coefficient of variation (CV)1.0454698
Kurtosis-1.8759492
Mean3.9476831
Median Absolute Deviation (MAD)0
Skewness0.14355133
Sum2886869.6
Variance17.033644
MonotonicityNot monotonic
2024-01-01T21:06:08.368500image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 376196
51.4%
10 24906
 
3.4%
9 15803
 
2.2%
8 12942
 
1.8%
8.5 5684
 
0.8%
7 5409
 
0.7%
9.5 4295
 
0.6%
8.33 3238
 
0.4%
7.5 3187
 
0.4%
8.67 3150
 
0.4%
Other values (721) 276472
37.8%
ValueCountFrequency (%)
0 376196
51.4%
1 387
 
0.1%
1.01 1
 
< 0.1%
1.06 2
 
< 0.1%
1.17 1
 
< 0.1%
1.2 1
 
< 0.1%
1.21 1
 
< 0.1%
1.25 1
 
< 0.1%
1.26 1
 
< 0.1%
1.27 1
 
< 0.1%
ValueCountFrequency (%)
10 24906
3.4%
9.99 47
 
< 0.1%
9.98 50
 
< 0.1%
9.97 59
 
< 0.1%
9.96 64
 
< 0.1%
9.95 78
 
< 0.1%
9.94 89
 
< 0.1%
9.93 99
 
< 0.1%
9.92 114
 
< 0.1%
9.91 94
 
< 0.1%

watching
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct553
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7657142
Minimum0
Maximum4358
Zeros406513
Zeros (%)55.6%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2024-01-01T21:06:08.535499image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile20
Maximum4358
Range4358
Interquartile range (IQR)4

Descriptive statistics

Standard deviation20.49589
Coefficient of variation (CV)4.3006965
Kurtosis5234.6296
Mean4.7657142
Median Absolute Deviation (MAD)0
Skewness44.342809
Sum3485081
Variance420.08152
MonotonicityNot monotonic
2024-01-01T21:06:08.687536image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 406513
55.6%
1 61276
 
8.4%
2 41558
 
5.7%
3 32552
 
4.5%
4 26306
 
3.6%
5 21105
 
2.9%
6 17225
 
2.4%
7 14021
 
1.9%
8 11912
 
1.6%
9 10020
 
1.4%
Other values (543) 88794
 
12.1%
ValueCountFrequency (%)
0 406513
55.6%
1 61276
 
8.4%
2 41558
 
5.7%
3 32552
 
4.5%
4 26306
 
3.6%
5 21105
 
2.9%
6 17225
 
2.4%
7 14021
 
1.9%
8 11912
 
1.6%
9 10020
 
1.4%
ValueCountFrequency (%)
4358 1
< 0.1%
2934 1
< 0.1%
2579 1
< 0.1%
2481 1
< 0.1%
2301 1
< 0.1%
2227 1
< 0.1%
2035 1
< 0.1%
1808 1
< 0.1%
1705 1
< 0.1%
1546 1
< 0.1%

completed
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2477
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.953066
Minimum0
Maximum13226
Zeros369391
Zeros (%)50.5%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2024-01-01T21:06:08.842502image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q348
95-th percentile347
Maximum13226
Range13226
Interquartile range (IQR)48

Descriptive statistics

Standard deviation186.63329
Coefficient of variation (CV)2.8297894
Kurtosis226.61113
Mean65.953066
Median Absolute Deviation (MAD)0
Skewness9.4152518
Sum48230290
Variance34831.984
MonotonicityNot monotonic
2024-01-01T21:06:09.012501image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 369391
50.5%
1 22098
 
3.0%
2 12573
 
1.7%
3 9550
 
1.3%
4 8270
 
1.1%
5 7153
 
1.0%
6 6418
 
0.9%
7 5734
 
0.8%
8 5361
 
0.7%
9 5030
 
0.7%
Other values (2467) 279704
38.2%
ValueCountFrequency (%)
0 369391
50.5%
1 22098
 
3.0%
2 12573
 
1.7%
3 9550
 
1.3%
4 8270
 
1.1%
5 7153
 
1.0%
6 6418
 
0.9%
7 5734
 
0.8%
8 5361
 
0.7%
9 5030
 
0.7%
ValueCountFrequency (%)
13226 1
< 0.1%
11643 1
< 0.1%
9996 1
< 0.1%
9833 1
< 0.1%
9640 1
< 0.1%
8412 1
< 0.1%
8313 1
< 0.1%
7893 1
< 0.1%
7668 1
< 0.1%
7657 1
< 0.1%

on_hold
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct477
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.391615
Minimum0
Maximum5167
Zeros531300
Zeros (%)72.7%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2024-01-01T21:06:09.172499image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile17
Maximum5167
Range5167
Interquartile range (IQR)1

Descriptive statistics

Standard deviation19.296913
Coefficient of variation (CV)5.6895941
Kurtosis14478.702
Mean3.391615
Median Absolute Deviation (MAD)0
Skewness77.812762
Sum2480227
Variance372.37084
MonotonicityNot monotonic
2024-01-01T21:06:09.337502image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 531300
72.7%
1 39814
 
5.4%
2 24903
 
3.4%
3 18027
 
2.5%
4 14128
 
1.9%
5 11217
 
1.5%
6 9153
 
1.3%
7 7713
 
1.1%
8 6584
 
0.9%
9 5626
 
0.8%
Other values (467) 62817
 
8.6%
ValueCountFrequency (%)
0 531300
72.7%
1 39814
 
5.4%
2 24903
 
3.4%
3 18027
 
2.5%
4 14128
 
1.9%
5 11217
 
1.5%
6 9153
 
1.3%
7 7713
 
1.1%
8 6584
 
0.9%
9 5626
 
0.8%
ValueCountFrequency (%)
5167 1
< 0.1%
4493 1
< 0.1%
3463 1
< 0.1%
2859 1
< 0.1%
1986 1
< 0.1%
1952 1
< 0.1%
1937 1
< 0.1%
1751 1
< 0.1%
1693 1
< 0.1%
1685 1
< 0.1%

dropped
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct654
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5654795
Minimum0
Maximum14341
Zeros539661
Zeros (%)73.8%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2024-01-01T21:06:09.506536image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile22
Maximum14341
Range14341
Interquartile range (IQR)1

Descriptive statistics

Standard deviation34.915341
Coefficient of variation (CV)7.6476833
Kurtosis49703.871
Mean4.5654795
Median Absolute Deviation (MAD)0
Skewness158.56218
Sum3338653
Variance1219.0811
MonotonicityNot monotonic
2024-01-01T21:06:09.661499image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 539661
73.8%
1 35417
 
4.8%
2 21957
 
3.0%
3 15790
 
2.2%
4 12176
 
1.7%
5 9903
 
1.4%
6 8069
 
1.1%
7 6826
 
0.9%
8 5875
 
0.8%
9 5080
 
0.7%
Other values (644) 70528
 
9.6%
ValueCountFrequency (%)
0 539661
73.8%
1 35417
 
4.8%
2 21957
 
3.0%
3 15790
 
2.2%
4 12176
 
1.7%
5 9903
 
1.4%
6 8069
 
1.1%
7 6826
 
0.9%
8 5875
 
0.8%
9 5080
 
0.7%
ValueCountFrequency (%)
14341 1
< 0.1%
7509 1
< 0.1%
7354 1
< 0.1%
7172 1
< 0.1%
5753 1
< 0.1%
5354 1
< 0.1%
4798 1
< 0.1%
3206 1
< 0.1%
2853 1
< 0.1%
2637 1
< 0.1%

plan_to_watch
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct1497
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.547893
Minimum0
Maximum21804
Zeros472247
Zeros (%)64.6%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2024-01-01T21:06:09.845534image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35
95-th percentile87
Maximum21804
Range21804
Interquartile range (IQR)5

Descriptive statistics

Standard deviation90.286927
Coefficient of variation (CV)5.1451721
Kurtosis6264.5793
Mean17.547893
Median Absolute Deviation (MAD)0
Skewness45.407129
Sum12832458
Variance8151.7292
MonotonicityNot monotonic
2024-01-01T21:06:09.995536image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 472247
64.6%
1 31728
 
4.3%
2 18376
 
2.5%
3 13353
 
1.8%
4 10968
 
1.5%
5 8990
 
1.2%
6 7987
 
1.1%
7 6912
 
0.9%
8 6414
 
0.9%
9 5606
 
0.8%
Other values (1487) 148701
 
20.3%
ValueCountFrequency (%)
0 472247
64.6%
1 31728
 
4.3%
2 18376
 
2.5%
3 13353
 
1.8%
4 10968
 
1.5%
5 8990
 
1.2%
6 7987
 
1.1%
7 6912
 
0.9%
8 6414
 
0.9%
9 5606
 
0.8%
ValueCountFrequency (%)
21804 1
< 0.1%
11677 1
< 0.1%
8863 1
< 0.1%
8041 1
< 0.1%
7922 1
< 0.1%
7770 1
< 0.1%
7426 1
< 0.1%
7337 1
< 0.1%
6878 1
< 0.1%
6672 1
< 0.1%

total_entries
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3095
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96.230147
Minimum0
Maximum24817
Zeros340242
Zeros (%)46.5%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2024-01-01T21:06:10.151535image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q374
95-th percentile507
Maximum24817
Range24817
Interquartile range (IQR)74

Descriptive statistics

Standard deviation265.45922
Coefficient of variation (CV)2.7585869
Kurtosis506.87469
Mean96.230147
Median Absolute Deviation (MAD)1
Skewness12.023183
Sum70371374
Variance70468.598
MonotonicityNot monotonic
2024-01-01T21:06:10.313537image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 340242
46.5%
1 28089
 
3.8%
2 13285
 
1.8%
3 9518
 
1.3%
4 8058
 
1.1%
5 6948
 
1.0%
6 6138
 
0.8%
7 5516
 
0.8%
8 4970
 
0.7%
9 4858
 
0.7%
Other values (3085) 303660
41.5%
ValueCountFrequency (%)
0 340242
46.5%
1 28089
 
3.8%
2 13285
 
1.8%
3 9518
 
1.3%
4 8058
 
1.1%
5 6948
 
1.0%
6 6138
 
0.8%
7 5516
 
0.8%
8 4970
 
0.7%
9 4858
 
0.7%
ValueCountFrequency (%)
24817 1
< 0.1%
24808 1
< 0.1%
20545 1
< 0.1%
19410 1
< 0.1%
18322 1
< 0.1%
17834 1
< 0.1%
16143 1
< 0.1%
13857 1
< 0.1%
13327 1
< 0.1%
12593 1
< 0.1%

rewatched
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct666
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4433515
Minimum0
Maximum13215
Zeros590483
Zeros (%)80.7%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2024-01-01T21:06:10.695502image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile22
Maximum13215
Range13215
Interquartile range (IQR)0

Descriptive statistics

Standard deviation29.693175
Coefficient of variation (CV)6.6826076
Kurtosis54759.642
Mean4.4433515
Median Absolute Deviation (MAD)0
Skewness138.50214
Sum3249343
Variance881.68462
MonotonicityNot monotonic
2024-01-01T21:06:10.858537image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 590483
80.7%
2 16284
 
2.2%
1 15046
 
2.1%
3 11339
 
1.6%
4 8343
 
1.1%
5 7532
 
1.0%
6 5859
 
0.8%
7 4942
 
0.7%
8 4192
 
0.6%
10 3931
 
0.5%
Other values (656) 63331
 
8.7%
ValueCountFrequency (%)
0 590483
80.7%
1 15046
 
2.1%
2 16284
 
2.2%
3 11339
 
1.6%
4 8343
 
1.1%
5 7532
 
1.0%
6 5859
 
0.8%
7 4942
 
0.7%
8 4192
 
0.6%
9 3658
 
0.5%
ValueCountFrequency (%)
13215 1
< 0.1%
3693 1
< 0.1%
3498 1
< 0.1%
3285 1
< 0.1%
2085 1
< 0.1%
2073 1
< 0.1%
2001 1
< 0.1%
1958 1
< 0.1%
1836 1
< 0.1%
1784 1
< 0.1%

episodes_watched
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct20568
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1658.8275
Minimum0
Maximum33764424
Zeros352107
Zeros (%)48.1%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2024-01-01T21:06:11.020535image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median15
Q31489
95-th percentile7355
Maximum33764424
Range33764424
Interquartile range (IQR)1489

Descriptive statistics

Standard deviation50771.684
Coefficient of variation (CV)30.606969
Kurtosis307483.21
Mean1658.8275
Median Absolute Deviation (MAD)15
Skewness521.43819
Sum1.2130707 × 109
Variance2.5777639 × 109
MonotonicityNot monotonic
2024-01-01T21:06:11.175538image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 352107
48.1%
1 2960
 
0.4%
26 2130
 
0.3%
13 1531
 
0.2%
24 1523
 
0.2%
12 1513
 
0.2%
25 1432
 
0.2%
2 1318
 
0.2%
37 1088
 
0.1%
50 901
 
0.1%
Other values (20558) 364779
49.9%
ValueCountFrequency (%)
0 352107
48.1%
1 2960
 
0.4%
2 1318
 
0.2%
3 887
 
0.1%
4 750
 
0.1%
5 648
 
0.1%
6 733
 
0.1%
7 481
 
0.1%
8 469
 
0.1%
9 393
 
0.1%
ValueCountFrequency (%)
33764424 1
< 0.1%
18218986 1
< 0.1%
16909587 1
< 0.1%
6193664 1
< 0.1%
5433345 1
< 0.1%
4331006 1
< 0.1%
3342576 1
< 0.1%
1963908 1
< 0.1%
1778924 1
< 0.1%
1638375 1
< 0.1%

Interactions

2024-01-01T21:06:01.685500image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:36.270681image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:38.852553image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:41.627500image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:44.498500image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:46.974501image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:49.487553image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:51.873552image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:54.271499image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:56.684504image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:59.108536image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:06:01.902526image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:36.511275image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:39.071504image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:41.844554image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:44.726536image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:47.193499image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:49.697500image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:52.093500image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:54.497500image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:56.912501image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:59.325499image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:06:02.118500image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:36.751322image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:39.486534image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:42.059500image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:44.951507image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:47.404536image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:49.904555image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:52.307502image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:54.711500image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:57.158502image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:59.767554image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:06:02.333500image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:36.970539image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:39.784510image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:42.266508image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:45.221507image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:47.632526image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:50.120555image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:52.517555image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:54.931499image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:57.371538image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:59.980539image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:06:02.544538image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:37.204534image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:40.065541image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:42.476498image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:45.471499image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:47.836504image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:50.326502image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:52.722499image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:55.145534image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:57.578501image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:06:00.191500image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:06:02.765554image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:37.462500image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:40.348554image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:42.695500image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:45.699503image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:48.046499image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:50.543501image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:52.953503image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:55.367503image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:57.791538image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:06:00.407555image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:06:02.984503image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:37.681500image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:40.557499image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:42.918500image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:45.907498image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:48.258554image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:50.760502image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:53.177499image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:55.628536image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:57.994534image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:06:00.610543image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:06:03.220534image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:37.911503image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:40.770508image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:43.191499image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:46.136549image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:48.475498image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:50.992501image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:53.399556image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:55.843502image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:58.214540image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:06:00.826558image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:06:03.430554image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:38.128501image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:40.971499image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:43.488502image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:46.337554image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:48.835501image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:51.203501image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:53.614500image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:56.041537image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:58.414528image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:06:01.037502image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:06:03.650501image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:38.350501image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:41.186500image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:43.810500image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:46.541535image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:49.046499image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:51.416499image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:53.828553image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:56.246557image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:58.631499image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:06:01.246554image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:06:03.865534image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:38.617499image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:41.400527image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:44.184502image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:46.748539image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:49.264534image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:51.645500image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:54.041502image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:56.461536image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:05:58.880554image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:06:01.465562image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2024-01-01T21:06:11.317537image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
iddays_watchedmean_scorewatchingcompletedon_holddroppedplan_to_watchtotal_entriesrewatchedepisodes_watchedgender
id1.000-0.297-0.287-0.272-0.283-0.221-0.207-0.215-0.304-0.199-0.3000.170
days_watched-0.2971.0000.7370.8500.9770.7000.7110.7760.9740.5420.9970.000
mean_score-0.2870.7371.0000.6580.7030.4290.3860.5220.7340.4120.7430.289
watching-0.2720.8500.6581.0000.8290.6430.6250.7410.8680.4360.8510.009
completed-0.2830.9770.7030.8291.0000.7080.7260.7950.9760.5070.9730.041
on_hold-0.2210.7000.4290.6430.7081.0000.7010.7080.7190.4250.6970.009
dropped-0.2070.7110.3860.6250.7260.7011.0000.6890.7250.4160.7080.001
plan_to_watch-0.2150.7760.5220.7410.7950.7080.6891.0000.8280.4290.7750.009
total_entries-0.3040.9740.7340.8680.9760.7190.7250.8281.0000.5050.9750.024
rewatched-0.1990.5420.4120.4360.5070.4250.4160.4290.5051.0000.5430.003
episodes_watched-0.3000.9970.7430.8510.9730.6970.7080.7750.9750.5431.0000.000
gender0.1700.0000.2890.0090.0410.0090.0010.0090.0240.0030.0001.000

Missing values

2024-01-01T21:06:04.319499image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-01T21:06:05.294502image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idnamegenderjoineddays_watchedmean_scorewatchingcompletedon_holddroppedplan_to_watchtotal_entriesrewatchedepisodes_watched
01xinilmale2004-11-05t00:00:00+00:00142.37.37123389364399608458
13aokaadomale2004-11-11t00:00:00+00:0068.67.3423137994440343154072
24crystalfemale2004-11-13t00:00:00+00:00212.86.681663630304510001012781
39arcane-2004-12-05t00:00:00+00:0030.07.715544306601817
418mad-2005-01-03t00:00:00+00:0052.06.27111410523153423038
520vondurmale2005-01-05t00:00:00+00:0073.18.0611941122013874374
623amuro-2005-01-23t00:00:00+00:00142.57.41202985195039208565
736bamanmale2005-02-05t00:00:00+00:00272.15.90271144115533815753616309
844beddanmale2005-02-21t00:00:00+00:0018.67.600370003701083
947kei-clonemale2005-03-09t00:00:00+00:0034.56.84151042231916312054
idnamegenderjoineddays_watchedmean_scorewatchingcompletedon_holddroppedplan_to_watchtotal_entriesrewatchedepisodes_watched
7312721291057imjustjkmale2012-05-06t00:00:00+00:00101.97.00318151934242197015
7312731291063leastu-2012-05-06t00:00:00+00:000.00.0000000000
7312741291065stasia-writer-2012-05-06t00:00:00+00:000.00.0000000000
7312751291071fiswoul-2012-05-06t00:00:00+00:000.00.0000000000
7312761291079dybidomale2012-05-06t00:00:00+00:0041.08.48288753313503317
7312771291083dolopa-2012-05-06t00:00:00+00:000.00.0000000000
7312781291085alenrobnik-2012-05-06t00:00:00+00:0021.48.221658914212601239
7312791291087oblongata-2012-05-06t00:00:00+00:0051.37.53381750921143323010
7312801291091etnota-2012-05-06t00:00:00+00:000.00.0000000000
7312811291097juunanasaifemale2012-05-06t00:00:00+00:003.79.67111002140222